Improving the selection of feature points for tracking

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    13 Citations (Scopus)

    Abstract

    The problem considered in this paper is how to select the feature points (in practice, small image patches are used) in an image from an image sequence, such that they can be tracked adequately further through the sequence. Usually, the tracking is performed by some sort of local search method looking for a similar patch in the next image in the sequence. Therefore, it would be useful if we could estimate ‘‘the size of the convergence region’’ for each image patch. There is a smaller chance of error when calculating the displacement for an image patch with a large convergence region than for an image patch with a small convergence region. Consequently, the size of the convergence region can be used as a proper goodness measure for a feature point. For the standard Kanade-Lucas-Tomasi (KLT) tracking method, we propose a simple and fast way to approximate the convergence region for an image patch. In the experimental part, we test our hypothesis on a large set of real data.
    Original languageUndefined
    Pages (from-to)144-150
    Number of pages7
    JournalPattern analysis and applications
    Volume7
    Issue number2
    DOIs
    Publication statusPublished - Jul 2004

    Keywords

    • Robustness
    • Feature (interest) point selection Motion estimation Visual tracking Optical flow Convergence region
    • IR-48637
    • METIS-220267
    • Visual tracking
    • Feature (interest) point selection
    • Convergence region
    • motion estimation
    • EWI-17483
    • Optical Flow

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